Roles of various brain structures on non-invasive lateralization of temporal lobe epilepsy

Abstract

In this paper, we evaluate roles of different brain structures on lateralization of the epileptogenic focus in temporal lobe epilepsy (TLE) patients based on imaging features. To this end, we extract volumes of multiple brain structures from preoperative images of a retrospective cohort of seventy-five TLE patients with surgical outcome of Engel class I. Then, we apply data mining techniques such as feature extraction, feature selection, and machine learning classifiers. Exploiting volumes of various structures and two machine learning classifiers, we examine contributions of brain structures and classifiers to the lateralization of TLE patients. Our experiments, using volumes of hippocampus and amygdala, show correct lateralization rates of 86.7% to 93.3% for decision tree and support vector machine (SVM) classifiers. This reflects 6.7% to 10.6% improvement in accuracy relative to using hippocampus volume alone. Also, using volumes of hippocampus, amygdala, and thalamus, we reach correct lateralization rate of 96.0% for SVM. Rules extracted from decision tree indicate that for intermediate hippocampus volumes, amygdala enlargement may determine side of epileptogenic focus. In conclusion, classification of the selected brain structures using the proposed classifiers improve decision-making of surgical resection in TLE and may reduce the need for implantation of intracranial monitoring electrodes.